Summary of "Cypher 2025 - Day 3 | Asia’s Biggest AI & Data Science Conference in Bengaluru | AIM Network| Hall 1"

1. Association of Data Scientists and Bespoke AI Training Programs

Founded in 2012, the Association of Data Scientists (ADaSci) has grown to over 40 countries.
Main differentiator: Bespoke training programs tailored to organizational needs considering current AI landscape.
Training levels cover awareness, immersion, proficiency, and expert levels.
Industry experts guide learners with practical, use-case-driven training rather than just theory.
Past collaborations include large companies like EXL, Bane Bank of Bodha, Honeywell, EY.
Development of a Capability Maturity Model (CMM) to assess AI proficiency and recommend suitable training.
Delivery modes: instructor-led (in-person/virtual), self-paced learning via a dedicated Learning Management System (LMS).
LMS access continues post-training for ongoing learning.
Soft skills training and industry immersion with CXOs and VPs as guest speakers.
Program domains include Agentic AI, cloud and data engineering (AWS, GCP, Azure), AI solution architecting, generative AI, LLMs, RAG, vector databases, fine-tuning, quantization.
AIM stall available for queries about programs.

India, with 1.4 billion people and diverse languages, is at a technological cusp, especially in AI adoption.
India has built a robust Digital Public Infrastructure (Aadhaar, UPI, DigiLocker, etc.) enabling massive financial inclusion and innovation.
Challenges include fragmentation in financial services due to siloed data, regulatory complexity, and cross-border issues.
Vision for borderless banking driven by AI, cloud-native infrastructure, and digital-first strategies.
Basic financial needs are universal: easy access, payment, saving, credit, investment, and insurance.
Three pillars for future financial architecture:
- Interoperability by design (e.g., Aadhaar and UPI as platforms and protocols).
- Contextual intelligence (data as a compass, not just oil).
- Global-local fusion (leveraging India’s scale and complexity for global innovation).
Need for a global digital financial identity akin to a passport to enable seamless cross-border financial services.
Emphasis on collaboration, API economy, and building bridges rather than moats.

Traditional synergy models (asset-based, financial) are less relevant; learning velocity and data-driven intelligence are key.
Conglomerates can leverage data, algorithms, and customer insights across businesses for competitive advantage.
Portfolio logic in AI era: focus on data complementarities, algorithm transferability, and customer identity overlap.
Operating model: empower autonomous business units with shared platforms and AI centers of excellence.
Governance and trust architecture critical for data sharing, consent management, and compliance.
Talent management: AI talent as a shared, mobile resource across businesses.
Measurement of AI impact via “Intelligence P&L” metrics (reuse ratio, time to scale, uplift).
Conglomerates can transform into platform orchestrators, enabling innovation at scale.

AI revolutionizing collections by scaling outreach, reducing costs, and embedding compliance.
Shift from one-dimensional collection efficiency to a tri-vector model: efficiency, cost, compliance.
Agentic AI replicates human empathy and judgment at scale, handling millions of interactions daily.
AI-human collaboration: AI handles routine cases; humans intervene in complex, sensitive situations.
Hyper-personalization via AI orchestrators controlling multi-channel outreach.
Challenges include regional language diversity, accents, and culturally relevant interactions.
Regulatory frameworks like RBI’s AI governance are essential to ensure trust, transparency, and auditability.
Indian market specifics: low-value transactions, rural outreach, and compliance are key focus areas.
Future: AI will increasingly take over collections, with humans focusing on last-mile intervention.

Regulation is the biggest risk for AI startups and industries; policy failures can cause existential damage.
Innovation and policy operate at different speeds; governments are “ships,” industries are “skaters.”
Proactive engagement with policymakers is essential to build trust and avoid prohibitive regulations.
Lessons from gaming: lack of early trust frameworks led to industry bans and loss of jobs.
AI industry must embed trust frameworks early around user safety, economic value, and national interests.
Governments need education